Medical accountable provider platform

ABSTRACT

The technology described herein relates to using predictions about patients&#39; future health care utilization and/or outcomes (e.g., patients&#39; expected future adherence to medication regimens) and the expected economic benefits of targeted improvements in the same utilization and/or outcomes (e.g., reduced likelihood of hospitalization attributable to more consistent medication use) to implement more effective and efficient health care improvement programs. The technology described here computes which subset of patients should be included in a value-based health care provider payment scheme and what the specific bonus payment amounts should be such that expected benefits from better patient outcomes, once realized, are greater than the expected costs of the payment scheme itself.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims priority to U.S. Patent Application No.61/841,395, filed Jun. 30, 2013, the entire contents of which areincorporated herein by reference.

BACKGROUND AND SUMMARY

Financial incentive programs have been available in the medicalprofession for quite some time. For example, pay-for-performanceprograms are incentive-based programs that reward members in the healthcare community (e.g., doctors, hospitals, pharmacies) for meetingpre-established targets for delivery of healthcare services. Thesetargets for delivery of healthcare services could cover a variety offields in the healthcare industry including, but not limited to,adherence to one or more medication regimens, reduction of hospitalreadmissions, and/or timely participation in medical detection processes(e.g., receiving a mammogram when the patient is of a certain age).

In fact, there are many examples in health care of financial incentives(or negative incentives/penalties) offered by health insurers,pharmacies, pharmacy benefit managers, health care delivery systems, orpharmacy chains to health care professionals in exchange for performingparticular services at defined levels. For example, a bonus can beachieved when a health care provider ensures that a certain percentageof the eligible patient population adheres to a drug therapy (e.g.,blood pressure medication) according to clinical guidelines. Incentivescan also be achieved based on defined performance quality measures. Forexample, a bonus can be achieved when a health care provider ensuresthat average blood sugar levels among eligible patients do not surpasscertain levels.

Bonus payments are typically calculated as a function of an availablebonus pool (e.g., an amount of money) divided by the number of expectedor potential bonus payouts. In other cases, the magnitude of bonuspayments may, for example, be simply benchmarked to industry norms orset as an amount that is believed to be conducive to induce providerengagement and participation.

However, there are certain drawbacks to these programs. For example,these incentive programs are generally applicable to a large populationof patients where the rewards are distributed to healthcareprofessionals on behalf of the measured performance of patients acrossthe population. That is, the bonus pool is allocated evenly for eachindividual in the population thus making the incentive associated witheach individual relatively small when the population is large.

A fundamental shortcoming in the art is a lack of technology or set ofanalytic processes that systematically calculates and offers a financialbonus payment to a health care professional that is determined by thefactors mentioned above, as well as the predicted risk profiles ofpatients eligible for inclusion in quality measures and the expectedfinancial or economic benefits that could accrue if patients achieveperformance goals as intended by the bonus payment scheme itself (e.g.,if population-level increases in medication adherence are achieved asintended by the bonus available to relevant health care professionals,the average expected cost of those patients will fall by a specific,predictable amount). Another shortcoming in the art is that there is notechnology that is directed to prediction-driven or prediction-derivedoutcomes payments to health care professionals designed to return theincreased (or even maximized) economic gains while reducing (or evenminimizing) program and bonus payment costs. Thus, there is a need for asystem that improves upon these drawbacks and provides for solutionsmentioned above.

The technology described herein relates to using predictions aboutpatients' future health care outcomes (e.g., patients' expected futureadherence to medication regimens) and the expected economic benefits oftargeted improvements on certain performance measures (e.g., reducedlikelihood of hospitalization attributable to more consistent medicationuse). Certain technology related to predicting patients future healthcare outcomes is described in commonly assigned U.S. patent applicationSer. No. 13/729,817, the entire contents of which are incorporatedherein by reference. These factors help compute which subset of patientsshould be included in a payment scheme and what the specific bonuspayment amounts should be such that expected benefits from betterpatient outcomes, once realized, outweigh the expected costs of thepayment scheme itself. While the description herein is made with respectto a medication adherence example, it will be understood that thedescription in this regard is illustrative and non-limiting. Thetechnology described herein may be applicable to any of a wide range ofconditions and incentive programs.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustrative block diagram of a system for a customprovider incentive protocol;

FIG. 2 is an illustrative block diagram of an example setup procedureused to define a prediction function in accordance with an exampleembodiment;

FIG. 3 is an illustrative block diagram of a custom provider incentiveprotocol;

FIG. 4 is an illustrative flowchart for deriving a custom providerincentive protocol;

FIG. 5 is an illustrative schematic block diagram of elements of acomputing system that may be used to perform functions associated withan example embodiment;

FIG. 6 shows a non-limiting example user interface of the system;

FIG. 7 shows a non-limiting example user interface showing patientlistings in the customer provider incentive system;

FIG. 8 shows a non-limiting example user interface providing furtherpatient details;

FIG. 9 shows a non-limiting example user interface providing furtherpatient details;

FIG. 10 shows a non-limiting example user interface providing summaryinformation;

FIG. 11 shows a non-limiting example user interface providing furthersummary information; and

FIG. 12 shows a non-limiting example user interface including practicesummary information.

DETAILED DESCRIPTION OF THE TECHNOLOGY

Most health services are paid for on a fixed fee basis (i.e., “fee forservice”), but various forms of performance or value-based payment havebeen adopted in recent years. For example, certain health care paymentmodels typically make uniform bonus payments available to health careprofessionals in exchange for performing certain tasks, ensuring certainprocesses, or contributing to certain outcomes. However, these modelshave several deficiencies. As an example, bonus payment programs andrelated performance measures tend to require that services are rendered,or targeted performance improvements are attained across large numbersof patients who meet certain criteria (e.g., all patients diagnosed witha particular condition). Consequently, available bonus dollars for theseprograms are distributed over relatively large populations and, hence,incremental bonus dollars available per patient tend to be very small.This is disadvantageous because it reduces the incentive for practicesto engage in the bonus payment programs due to the large number ofpatients the practice will be required to monitor or engage in order toobtain the bonus and incentivize the practice to participate in theprogram.

With very large populations of patients included in such programs,doctors, pharmacists and other health care professionals are faced withvery large numbers of patients whose care must be better managed inorder to earn the available potential bonus payments. The potential toincur substantial cost and expend resources, and related logisticalchallenges in providing extra support to large numbers of patients withdisproportionately low incentive payment may often result in health careprofessionals being less likely to participate in bonus paymentprograms, rendering them ineffective.

Moreover, since such potential bonus payments can be relatively small(especially where relatively large numbers of patients are included),health care professionals often conclude that the benefits ofparticipating or undertaking extra efforts to achieve the performancegoals of a payment scheme overshadow the potential to earn smallpotential bonus payments. Thus, payment schemes designed to inducechanges in the behavior of health care professionals have often returneddisappointing results.

Furthermore, bonuses can also often be earned by providers when patientsare healthier—even if the provider played little or no role in achievingthe healthy outcome. Thus, what is needed is a system that increases theefficiency of these programs in a way to find a patient population thatcan (a) exclude patients from bonus programs who appear highly likely toachieve the program's goals without additional intervention and (b)exclude patients that are highly unlikely to achieve the program goalseven with additional intervention.

The technology described herein improves upon such disadvantages and isdirected to deriving and using predictions about patients' future healthcare outcomes (e.g., patients' expected future adherence to medicationregimens) and the expected economic benefits of targeted improvements oncertain performance measures. The system uses predictions aboutpatients' future health care outcomes (e.g., patients' expected futureadherence to medication regimens) and the expected economic benefits oftargeted improvements on certain performance measures (e.g., reducedlikelihood of hospitalization attributable to more consistent medicationuse) to determine which subset of patients should be included in thepayment scheme and what the specific bonus payment amounts should besuch that expected benefits from better patient outcomes, once realized,outweigh the expected costs of the payment scheme itself. That is, thetechnology is capable of narrowing the population of patients to asubset of (a) patients that are unlikely to achieve a desired healthoutcome without intervention and (b) patients who, if achieve thedesired health outcome, would aid in maximizing the benefit of theincentive program. Additionally, the technology is also capable ofattributing certain patients to one or more practitioners and thusidentifying practitioners that are beneficial to a particular incentiveprogram (e.g., aid in maximizing the benefit of the incentive program).

The system provides timely, clinically actionable information to healthcare professionals that can enrich and inform provider/patientinteractions in accordance with patients' risk profiles derived frompredictive algorithms. The system also facilitates, computes, andmanages prediction-derived, financial incentives for providers to helptheir patients improve medication adherence.

The system combines patient-level predictive algorithms regarding, forexample, medication adherence and other quality-related outcomes, dataon other patient characteristics derived from available data sources,and exogenously determined financial and other capacity or workflowconstraints or inputs from the end user (client). The result is aniterative system of prediction, patient risk assessment, interventiondecision support, and learning that achieves efficient population-levelimprovement in, for example, adherence and other medication qualitymeasures.

Included in the system are certain core elements. First, predictiveanalytics is used to identify a preferably optimal subset of a largerpatient population whose cumulative future predicted modifiable healthcare outcomes create the improved, and preferably, largest possibleeconomic gains to the sponsor of the bonus program. Analytics are usedto compute the magnitude of incremental bonus payments optimizing costreduction goals of the sponsor of the bonus program and increasedeconomic gains attributable to improved targeted health outcomeimprovements by the selected patients. The system can also employidentification and visual display, for example, via a web-based portalavailable to health care professionals eligible for bonus payments, ofpatient-specific information regarding the risk profiles of selectedpatients, the potential causes of their sub-optimal performance onselected medication quality measures, including, for example, adherence,and suggested action steps to achieve targeted improvements in selectedperformance indicators. Of course, the system is not limited toproviding this information using a visual display and can generate thedesired information and convey/transmit the information to a clientand/or third party (e.g., for display by the client/third party). Thisinformation could be transmitted using, for example, an electronicmedical record.

The technology establishes the basis for prediction-driven performanceimprovement programs in health care that have the potential to improvethe prevalence and performance of health care financial bonus programsfor health care professionals designed to achieve targeted improvementsin health care outcomes. For example, in the case of prescriptionmedication adherence, the system further includes medication adherencecommensurate with the value that those targeted improvements create tosponsors of such programs.

FIG. 1 is an illustrative block diagram showing elements of an exampleembodiment. The basic elements 10 are described very generally withrespect to FIG. 1 and will be described in further detail below withrespect to the remaining drawings. The system includes, for example,candidate patient data 100, which is processed as described herein andprovided to a prediction engine 200. The prediction engine 200 isdeveloped as described below and includes development of a predictionfunction which is based, at least in part, on an analysis of historicalpatient data 150 which may include historical data on, for example,intervention performance (for example, and without limitation, trackingdata). When the prediction function of the prediction engine 200 isapplied to the candidate patient data 100, a tailored patient-specificscore is output and can be provided to a custom provider incentiveprotocol 300. As described further below, the custom provider incentiveprotocol 300 can provide patient-specific tailored medical incentivesfor health care providers.

FIG. 2 shows an illustrative block diagram of an example setup procedureused to define a prediction function in accordance with an exampleembodiment. As illustrated in FIG. 2, historical patient data 210, whichmay be in the form of a retrospective data file (or multiple files) fromthe implementation entity or program sponsor, is provided. Data in thesefiles may include, for example, demographic, survey, clinical and/oradministrative claims data about patients who would have been candidatesfor the adherence program, plus filled prescriptions data to allow forcalculation of actual adherence after initiation of the medicationtherapy of interest. It will be understood that the data sources mayvary and may include other example data, such as, for example,administrative claims, electronic medical records, lab results, patientsurveys, sociodemographic detail, consumer purchasing data, etc. It willalso be understood that different and multiple sources of data may beused to determine any number of independent variables for use indeveloping the predictive function described below.

The historical patient data 210 is analyzed and used during setup 220 todefine certain variables 225, 230 and 235, for example, which may beused to determine a prediction function 280. For example, certain timeperiods 225 may be defined to assist in the extraction of variousvariables. Time periods of interest 225 may include, for example, aninception window or range of dates between filled prescriptions for thetarget medication which may be used to identify candidate patients forthe adherence program. An index date for each patient which reflects thedate of the first filled prescription for the target medication duringthe inception window may also be defined. It may also be useful todefine a look-back period during which the pre-index information iscollected about each patient. A common example look-back period is oneyear, but it will be understood that the look-back period can range fromzero days to many years. In addition, a follow-up period may be defined.A common follow-up period may typically be one year, but the follow-upperiod may range from one day to many years.

It is next preferable to define a dependent variable 230, sometimes alsoreferred to as the measure to be predicted. In the medication adherenceexample, a common measure of medication adherence is the binary outcomeof a proportion of days covered being greater than or equal to eightypercent. In other words, this dependent variable 230 would be satisfiedif the patient obtained sufficient prescriptions to have the medicationon hand for at least eighty percent of the days in the follow-up period.It will be understood that definitions of adherence may vary and thatthe definition set forth above is made by way of illustrativenon-limiting example. Other examples may include, proportion of dayscovered, medication possession ratio, discontinuation, etc.Additionally, threshold values may likewise vary,e.g., >=80%; >=70%; >=60%, etc. With the dependent variable 230 defined,it may then be useful to define a number of independent variables 235.It should also be appreciated that the proportion of days covered couldbe a dependent variable itself (e.g., instead of proportion of dayscovered being a binary calculation when compared against a threshold).

Independent variables 235, may be developed based on published studiesof factors associated with adherence in a particular therapy area. Thesemay, for example, fall into three broad category areas. One broadcategory area may be, for example, attributes of the patient, e.g., age,sex, county of residence, health status and prior health careutilization. Another broad category may include, for example, attributesof the target drug regimen, e.g., specific drug, strength, quantitydispensed, cost, etc. A third broad category may include, for example,attributes of the health care system, e.g., prescriber's specialty,number of pharmacies used, health plan design, etc.

Independent variables 235 may include variables known to be predictiveof adherence and may include variables not known to be associated withadherence, but which can be rapidly tested using data mining methods toderive these from the patient data itself. For example, software runningon the computer system 500 may be used to automatically createindependent variables. Other independent variables may include complexinteractions between various predictors. These variables generally mayrelate to the presence/absence and frequency of all possible drugs,diagnoses and procedures in the patient look-back period. Survey datamay also be included in the provided data and all possible responses mayalso be included. It will be understood that different and multiplesources of data may be used to determine any number of independentvariables for use in developing the predictive function.

A resultant data file 250 is generated to include the setup variables220 that are generated as described above. Patient data files may beaugmented and restructured to provide a common data structure for theresultant data file 250 in order to more efficiently process the datacontained in the resultant data file 250. The resultant data fileincludes information for each patient of interest from the historicalpatient data files 210 and include the setup variables 220 discussedabove.

Once the resultant data file 250 is created, it is provided to a furthermodel setup procedure 260, that will result in a prediction function 280that is then applied to candidate data to produce a patient-specificscore, in this case an adherence score. Creation of the predictionfunction 280 is discussed in more detail herein.

In general, the resultant data file is analyzed using multiplestatistical methods to develop the best predictive function when testedand validated against the historical patient data in view of the factthat actual adherence can be determined with respect to the historicalpatient data. As a specific example, the resultant data file 250 may bedivided into two parts: a “training” data file 265 and a “validation”data file 270. The system may then use, for example, multiplestatistical methods 275, including, for example, any one or more of thefollowing: logistic regression, random forests, classification andregression trees (CART), stacking, boosting, or the like, on thetraining data 265. These statistical methods may generally be combined,and as such, be referred to as ensemble methods 275 for determining orcreating a model or predictive function based on the training data 265that will yield highly predictive results. For example, as noted above,the resultant data file 250 may be partitioned into two parts, the“training” data 265 and the “validation” data 270 as described above.For the purposes of example, the resultant data may be randomlypartitioned so that eighty percent (80%) are the “training” data 265 andthe remaining twenty percent (20%) of the resultant data are in the“validation” data 270 set. It will be understood that any statisticallyproper partitioning may be selected based on the type of analysis andregression to be applied to the data. The models or predictive functionsare developed and tested using the training data 265 over multiplestatistical methods as discussed above (e.g., ensemble methods), andthen testing the predictive function against the validation data 270,which is also commonly referred to as held-out data. A predictivefunction that is derived from the training data 265 may then be appliedto the validation data 270, and the predictive function that isdetermined to perform best on the validation data 270 is selected as thepredictive function to be applied to the candidate patient data asdescribed below. It will be understood that any number of other possiblestatistical methods may be used to generate the resulting predictionfunction, and that the system and method disclosed is not limited to theparticular example statistical methods described herein.

In this manner, the independent variables or predictors are used topredict the dependent variable using the predictive function 280 createdon the basis of the historical patient data 210. The predictive function280 is used in creating a patient-specific score representative, as anon-limiting example, of a patient's probability of being adherent. Forexample, the patient-specific score could be an integer value between 0and 100 representing a percent chance of a patient being adherent (e.g.,0.74 represents a 74% chance the patient will be adherent). This scoretakes into account the medical history of each patient, as discussedabove, as well as the other setup variables used to determine theprobability of a patient's adherence. Thus, each patient in a particularpractice will have a patient-specific adherence score generated usingthe above-mentioned factors and this score will be used, in part, tocreate a subset of patients in the practice that will have the highestlikelihood of helping a health care provider (e.g., health insurancecompany) achieve its performance goals.

FIG. 3 is an illustrative block diagram of a custom provider incentiveprotocol 300. The custom provider incentive protocol 300 can receivedata from the prediction engine 200 so that a custom tailored incentiveplan can be provided for a health care provider. This data couldinclude, for example, the patient-specific scores representing, forexample, the probability of each patient's adherence to a drug regimenand/or medical services. The customer provider incentive protocol 300can have a patient selection unit 310, an incentive determination unit320, an incentive applying unit 330, and/or a user interface 340.

Certain medical and health organizations (e.g., medical insurancecompanies) can be held to various government mandated standards. Thestandards can impose both bonuses and penalties for meeting (or notmeeting) the criteria set out in the standard. As a working example, agovernment agency could require a health care provider, such as amedical insurance company, to ensure that 75% of their enrolled patientsare timely taking their blood pressure medication within a given timeperiod (e.g., 1 year). If the health care provider meets the mandatedrequirements (e.g., 75% of patients timely taking blood pressuremedication), the government could issue a bonus incentive to theprovider. Likewise, the government could institute a penalty on theprovider (e.g., a negative incentive) if they do not meet therequirements.

The health care provider would then work with the medical practices(e.g., doctors and hospitals) to make sure that the patient populationmeets the particular standard. As explained above, motivating practiceswith larger patient populations to participate in such programs isdifficult because bonus payments are typically uniformly spread acrossthe population of patients. Furthermore, the larger the number ofpatients eligible to participate in this program, the more unlikely apractice will be able to manage their adherence. Thus, the protocol 300can take into account the requirements set by the standard for thehealth care provider in order to narrow the patient population givingthe provider and/or practice the highest probability of achieving thegoal.

In more detail, the patient selection unit 310 may select a group ofpatients eligible for a particular incentive. For example, a governmentagency could mandate that an insurance company require 75% of itspatients eligible for blood pressure medication to be adherent to theirdrug regimen. If the company already has 71% of its patients beingadherent, the patient selection unit 310 could select a group ofpatients to achieve the remaining 4% (or more). For example, the patientselection unit 310 may retrieve a list of the patient-specific scores,as discussed above, for each patient in a particular practice that istaking blood pressure medication. The patient-specific score would bereflective of the probability that the patient will adhere to his/herprescription within a given time frame. Thus, the patient selection unit310 can determine the subset of patients that give the company/practicethe highest chance of achieving at least the missing 4% in the goal.

The unit 310 can accomplish this, for example, and without limitation,by identifying a group of patients that are not necessarily fullyadherent to their blood pressure regimen, yet have a higher probabilitythan other patients of being adherent with some management from thepractice. That is, the list of patients would be within a range ofscores maximizing expected outcome, reflective of not being 100%adherent, yet having a higher probability of being adherent with sometailored assistance or intervention from a participating practice. Forexample, a practice may have a large number of patients that arecompletely adherent as well as a number of patients that are entirelynon-adherent. The patient selection unit 310 will help identify thepatients in the “middle ground” that are not entirely adherent, yet,based on their patient-specific score have a higher likelihood of beingadherent (e.g., with some help from the practice). Once this subset isdetermined, the protocol 300 can tailor the bonus incentives for thenarrowed group of patients in order to provide larger incentives to thepractice for each individual patient.

More specifically, the incentive determination unit 320 can provide datarelating to both the type of incentive (e.g., patients eligible forblood pressure medication) as well as the total amount of financialincentive allocated for the program (e.g., $5,000 for all patients). Forexample, if a bonus incentive for a given quarter is $5,000 for 75% ofthe patients adhering to their blood pressure regimen, and a practicehas 5,000 eligible patients, the incentive would normally be spreadacross each patient (i.e., $1 per patient that is adherent). However, itcan be extremely difficult for a practice to monitor 5,000 patients toensure that they are all adherent to their blood pressure medication.The patient selection unit 310 would thus “filter” the patients thatwould be highly adherent (as those patients would not need managing) aswell as the patients that have little to no likelihood of being adherent(i.e., patients that would not be adherent even if the practice managedthem). The selection of patients would result in a subset that is notentirely adherent but having a greater (e.g., highest) likelihood ofbeing adherent with management from the practice.

The incentive determination unit 320 may then apply customizedincentives for each patient. For example, if in trying to find the “4%”of patients to make up the required 75%, the unit 310 may select a groupof 100 patients that are not entirely adherent but have a highprobability of being adherent with enhanced management from thepractice. The incentive determination unit 320 could then determine thateach patient that is selected will have a bonus amount of $50 for beingadherent during a given time frame. Thus, the incentive determinationunit 320 advantageously applies a higher incentive to each patient inthe subset making it more likely that the practice will engage thepatient to ensure that the patient is adherent to their blood pressureregimen. Of course, the incentive determination unit does notnecessarily have to apply the bonus amount equally to the subset and cangive greater/lesser bonus amounts to each patient based upon certaincriteria. The incentive determination unit 320 can also apply otherfactors as well including time limit durations for when the incentive isactive (e.g., over a span of 3 months) as well as a thresholdpercentage/number of patients that must participate to receive theincentive. Again, conventional incentive programs normally distributethe incentive equally on a per-patient basis among the eligiblepatients. However, using the patient-specific score, the incentives canbe custom tailored so that higher incentives are associated with certainpatients based on their score. It should also be appreciated that theincentive could also be constructed so that a practice can be providedwith a panel of patients to manage and the incentive is paid only if therate of adherence across the specifically selected panel exceeds athreshold value (e.g., to allow for an even higher incentive amount forthe provider).

The incentive applying unit 330 then applies the incentive for eachpatient so that the practice will know how much incentive amount (e.g.,money) can be achieved by having the patient be adherent. Thus, theprotocol 300 can narrow the list of patients so that the narrowed listwill result in a much more manageable number for a practice to engage inachieving adherence while providing significantly greater bonusincentives for each patient that is adherent. This allows thepractitioners (e.g., doctors) to take advantage of the bonus paymentprogram by treating fewer fewer high risk/need patients to satisfy theminimum patient criteria while achieving the maximum bonus amount. Thiseffectively gives both the health care provider as well as the practicethe highest likelihood of achieving the goals mandated by the governmentprogram.

The incentives for each patient could be presented to a user via a userinterface 340. For example, the user interface 340 may preferably be aweb-based interface accessible by health care provider staff where alist of the subset of patients most eligible for a particular incentivecould be provided, as well as access to each patient's relevant medicalhistory. An example user interface is shown in FIGS. 6-12, described infurther detail below. It should be appreciated that the data may notnecessary need to be conveyed using a visual display (e.g., a userinterface) and could be compiled into a transferable record (e.g., anelectronic medical record) that could be provided/transmitted to aclient and/or third party. By providing custom tailored incentives foreach patient using the patient-specific score, the health care providerwould have incentive to participate in the incentive program therebyalso providing a greater degree of wellness for the patient.

FIG. 4 is an illustrative flowchart for deriving a custom providerincentive protocol. The flowchart illustrates a non-limiting example ofprocesses carried out by the system 10, and more specifically theprotocol 300.

The protocol 300 may first establish the boundaries of the incentiveincluding the overall amount for the incentive and the rules for beingeligible for the incentive (S410). Using the example above, if a healthcare provider is required to have 75% of its patients adherent to bloodpressure medication, and the provider currently has 71% of patientsbeing adherent, the protocol 300 can take into account the requiredadditional 4% needed to achieve the 75% goal in applying the incentive.Thus, the protocol 300 would apply an overall amount for an incentive(e.g., $5,000) over a smaller targeted population of patients in a givenpractice. Of course, certain other factors may be applied fordetermining the details of the incentive and are in no way limited to afinancial dollar amount and/or a generic class of patients.

Upon determining the incentive details, the protocol 300 may determinethe candidate patients eligible/targeted for the incentive (S420). Usingthe example above, the protocol 300 can determine which patients areeligible (or currently being prescribed) for blood pressure medication.Of course, other factors may apply that would potentially broaden ornarrow the list.

Once the list of eligible patients is determined, the protocol 300 mayapply the patient-specific score to eligible patients thereby alteringthe incentive amount for each patient (S430). Using the example above,the protocol 300 can find the patients having a score within a rangethat is not entirely adherent yet having a higher likelihood ofadherence (e.g., with help from management of the practice). Thus,instead of having a list of 5,000 patients that are currently taking, orare eligible for, blood pressure medication, the list could be reducedto, for example, 100 patients that will help the company and practiceachieve the remaining “4%.” Thus, health care providers couldeffectively narrow the list of patients that may need these servicesthus encouraging the provider and practice to participate in theprogram.

The protocol 300 can optionally alter the incentive amount tailored foreach patient based on their patient-specific score by allowing a user toprovide additional variables that could effectively alter the score andalter the incentive amount (S440). If further modification is required(S450) (e.g., based on various user input), the system can modify/updatethe incentives for the patients (S440). If no further modification isnecessary, the system can generate a list (or subset of the list) ofpatients together with associated incentives (S460). As a non-limitingexample, the list could be ordered by the patients having the highestassociated financial incentive amount. The list could also be truncatedto only show a subset of patients with at least a particular incentiveamount (e.g., $500 or more). This list of patients could be displayedusing the user interface 340, as discussed above and described ingreater detail below.

By providing this information to the health care provider, the providercan focus their efforts and encourage the patient to follow a particularmedical treatment protocol (or in some instances not seek any medicaltreatment) while discouraging the provider from encouraging patientsthat may not be of immediate need of these services/drug regimens. Thecustom tailored incentives would also more likely encourage a healthcare provider to participate in the incentive program as the list wouldmost likely produce a smaller subset of individuals required to need theparticular medical therapy while providing a significantly higherfinancial incentive per patient.

FIG. 5 shows an illustrative schematic block diagram of elements of acomputing system that may be used to perform functions associated withan example embodiment, which illustrates the basic requirements of sucha computer system 500. In FIG. 5, a bus 580 interconnects a processor510 with various hardware, firmware and software elements including, forexample a memory 520 which may be used to store historical and candidatepatient data as well as patient-specific patient-level data generated bythe example health care management system described herein. Moreover, itwill be understood that this memory is not necessarily integrated withthe computing system 500, but may be operatively coupled to the systemin any manner, including, for example, via a secured cloud, dedicatedlinks, the Internet, or the like.

The computer system 500 may also include a program memory 530 containingvarious instructions or application software that may be used to operatethe processor 510 and to process data, including, for example,machine-level code to run the basic operations of the processor as wellas software for implementing the illustrated health care managementsystem. It will be noted that the program memory 530 may also beintegrated in whole or in part with the processor 510 and is notnecessarily a separate element as shown in the drawings. The computersystem 500 may further include several interfaces that enable variousinteractions with the system 500. For example, a user interface 540 isprovided that allows operators to program the system 500 as well as toenter and manipulate data, and to extract information that may be storedin the memory 520 or other databases connected to the system 500. Theuser interface 540 may be, for example, in the form of a display andassociated input/output devices, such as, for example, a keyboard,mouse, gesture pad, or the like (not shown).

The system 500 may also include a communications interface 560 thatprovides connections for allowing the system 500 to receive and transmitinformation to and from external sources via various communicationslinks, including, for example, the Internet, an electronic healthrecords system, dedicated links, secure clouds, or the like. Forexample, the communications interface 560 may be used to receivehistorical patient data and candidate patient data or patient-levelfiles generated at another system or site. It is also contemplated thatthe system 500 may include peripheral devices 570, such as, for example,external memory, a printer, etc. While an example general computingsystem 500 has been described herein, it will be appreciated that thoseskilled in the art would be able to devise any suitable computing systemto achieve the objects and features described herein with respect to thedisclosed example health care management system.

FIGS. 6-12 illustrate example user interfaces for providing informationrelated to the patient medication adherence and provider incentivesystem as discussed above. FIG. 6 illustrates an example interfaceproviding, at least, a brief summary for the customized bonus incentivefor a practitioner having practitioner identification PRID as well asgeneral “welcome” information for introducing the user to the system. Ascan be seen in FIG. 6, the example interface shows the bonus “progress”for the practitioner PRID for a given quarter. In this example, thepractitioner PRID has a bonus payment remaining amount BPR of $2,750.00with a bonus payment missed amount BPM of $750. The practitioner alsohas 53 patients needing attention PNA to attend to for purposes ofachieving the customized bonus incentive amount. The bonus paymentremaining amount BPR may refer to the amount of eligible patients for apractitioner PRID in which bonus payment can be achieved where the bonuspayment missed amount BPM may refer to the bonus payment from the numberof patients that were not adherent (e.g., did not refill theirprescription within an allotted amount of time from their expectedprescription refill date, did not receive mammograms within an allottedwindow of time).

In the example of a BPM for medication adherence, practices may notobtain a bonus if a patient does not fill their prescription within therequisite number of days of the targeted medication asprescribed/indicated by their doctor/practitioner within a defined timeinterval and/or the practice indicates that the selected patient is nottheir patient (e.g., the practice notifies of an error in the underlyingclaims data that caused the system to wrongly attribute a given patientto that practice). The underlying system can accommodate performancemeasures far beyond adherence, and the specific rules governing thelinkage between action/outcome and bonus payment would be customized bythe system for each such instance. For example, the system could beconfigured so that a practice receives a target bonus if the practicesuccessfully converts a patient to a 90-day refill and/or removes thepatient from medications deemed to be unsafe as determined by somethird-party and recognized clinical standards entity and/ororganization. In such examples, the system could be configured toconstruct a bonus payment reflecting such a setting.

Thus, the system provides a simple and easy to understand metric as tohow much compensation a particular practitioner could earn for theirpatient population as well as how much compensation the practice hasmissed during a given time period (e.g., quarter). It should beappreciated that the time period of measurement is not limited to aquarter and could be any particular unit of time (e.g., monthly,bi-monthly, yearly).

FIG. 7 shows an example interface for displaying a list of patients inthe customized incentive program where each patient is shown with theirmedication adherence PMA, prescribing opportunity PO, and bonus paymentopportunity BPO. The interface shows the patients for a particularpractitioner PRID where each patient is listed with a patientidentification PID (e.g., the patient's first and last name). Thepatient ID can also include the patient's date of birth as well as anyother particular identification information for the patient (e.g.,social security number, insurance ID, driver's license number).

As described above, a health care provider (e.g., a health insurancecompany) could have multiple goals to achieve for its population ofpatients. These goals could be mandated by, for example, a governmentagency or could even be goals self-imposed by the organization. Thesegoals could relate to different therapies for each patient. Some ofthese therapies include, but are not limited to, drug regimens (e.g.,blood pressure medications, cholesterol medications, heart medications,etc.) as well as medical services (e.g., mammograms, prostate exams,etc.).

Using the example above, the health care provider may require 75% of itspatients be adherent for a particular therapy. If the health careprovider only has 71% of its patients being adherent, the protocol 300may identify a list of patients to make up the remaining 4% by lookingat the patient-specific score of patients. Thus, patients having a scorewithin a specified range will be selected in the subset of patients thatprovide a greater likelihood for the provider to meet its goals. Thisrange can be engineered for each health plan and practice and if thenumber of patients generated from this list is not big enough, the rangecan be widened. The resulting list can be shown, for example, in part inFIG. 7 where each patient having patient ID can have a patient-specificscore that falls within the “4%” range. It should be appreciated thatthe list of patients could include more/less patients than the onesrequired to achieve a particular goal and is not in any way limited toonly the patients selected for achieving the goal of a particularprogram.

In the example shown in FIG. 7, each patient ID is listed inalphabetical order based on the name of the patient. For each patient,the interface can show the patient medication adherence PMA, aprescribing opportunity PO, and/or a bonus payment opportunity for eachpatient PID. The patient medication adherence PMA shows how adherent apatient is to a drug regimen for treating a particular condition. Forexample, patient “Jane Armstrong” is shown having a health condition IDCID of “blood pressure” in which the patient is taking a certain drugregimen to treat the blood pressure condition. The interface can showthe days past (or days until) the next refill date RD. In this example,5 days have passed between the current date and the date that “JaneArmstrong” prescription should have been refilled. For example, “JaneArmstrong” may have had a next refill date of May 1, 2014 where thecurrent date could be May 6, 2014.

In this example, even though the patient is 5 days past his/her nextrefill date, the customized incentive program still leaves the patientlisted as being eligible for the bonus payment by a bonus eligibleindicator BEI. If the bonus payment is no longer eligible for a givenpatient and his/her condition, the interface will indicate such byshowing a bonus ineligible indicator BII. For example, patient “MikeArmstrong” has missed 15 days since the latest expected prescriptionrefill date RD and thus has not been adherent in refilling themedication to treat the “cholesterol” condition. Thus, “Mike Armstrong”has a bonus ineligible indicator BII representing that the bonusopportunity BPO has been missed for this quarter. It should beunderstood that the indicators BEI, BII can be represented, for example,using color schemes. For example, indicator BEI could be in the colorgreen indicating that a bonus payment is still possible where indicatorBII could be in the color red indicating that a bonus paymentopportunity has been missed. Likewise, different color indicators couldbe used as warning indicators. For example, if the patient is close tobeing outside of the refill range, the indicator BEI could change fromthe color green to the color yellow, before changing to indicator BII incolor red.

The interface in the example shown in FIG. 7 also shows the bonuspayment opportunity BPO for each patient that is adherent to theirparticular drug regimen. For example, if patient “Jane Armstrong” hasbeen refilling their medication to treat “blood pressure” in a timelymanner, the bonus payment opportunity BPO will pay $50 for thatparticular patient. The display likewise can show multiple therapieswhere each therapy may have its own bonus payment opportunity. Forexample, “Mike Armstrong” has blood pressure and cholesterol therapieswhere each bonus payment opportunity BPO is $50. As the opportunity forcholesterol has been missed in this quarter, the BPO for “MikeArmstrong” is $50 instead of $100.

The display likewise shows a prescribing opportunity PO for eachpatient. The prescribing opportunity PO could alert the practice ifthere is an opportunity to add, remove, or change a prescription that,based on other indicators in the patient's medical or pharmacy record,may be appropriate for their care plan. Examples may include a diabeticand hypertensive patient that is not on the evidence-supported ACE/ARBfor patients with those two conditions or a patient who is currentlyover 65 years of age and on a medication considered high-risk for theelderly. It should be appreciated that ACE-ARB can refer to two specificmedications used to treat hypertension. These two classes are theangiotensin receptor blockers (ARB drugs) and the angiotensin convertingenzyme inhibitors (ACE inhibitors). Both of these classes of drugs lowerblood pressure by blocking certain specific steps in therenin-angiotensin-aldosterone (RAA) chain.

Practices can earn a bonus if they address prescribing opportunitiesflagged in the portal. Prescribing opportunities can be noted with a redflag and, when addressed, can be changed to a green check-mark. Itshould also be appreciated that the display can be adjusted to showmore/less patients as well as more/less health conditions for eachpatient. The display can also be configured to show multiple bonuspayment opportunities BPOs for each payment with different payoutsrespectively. Of course, the display could be configured to includeother various information. For example, the display could also includepatients from multiple participating health plans. That is, if apractice serves two or more health plans, the system can be configuredto capture both plans as customers of the product, and then the patientsselected and displayed for the product could reflect patients from bothplans. This could be more advantageous for the practice because thepractice will have a single interface to use for viewing patients forboth (or all) payers.

Likewise, the display can be adjusted so that the patients areprioritized and/or ordered based on the number of medications/therapiesthey are engaged. For example, “Mike Armstrong” could be listed at thetop as he has multiple therapies (e.g., blood pressure and cholesterol).The system could also eliminate practices that do not have a high enoughpatient participation number thus making it difficult, if notimpossible, for the health care provider to achieve the needed 4%through that particular practice.

FIG. 8 shows an example interface for providing one or more details fora particular patient PID for a given practitioner PRID. Certaininformation that can be accessed/displayed in this interface includes,but is not limited to, patient assessment, patient information,additional notes for the patient, and key facts related to the patient.This example interface can also summarize the adherence factors for aparticular patient. For example, for patient “Jane Armstrong,” thepatient medication adherence PMA for “blood pressure” is shown where thepatient has missed 5 days since the next expected refill date. Thisinformation would likely match the information shown in FIG. 7, but“drilled down” to show more detail for the patient. The medicationadherence PMA can also include the specific type of medication MED, theremaining bonus payment opportunity BPO, a refill due date RDD, as wellas a prescriber PR. Here, patient “Jane Armstrong” has a 90 day supplyof Moexipril-Hydrochlorothiazide for blood pressure to be refilled onApr. 24, 2014 and prescribed by Dr. Smith. If the patient refills themedication within an allotted window of time before/after the refill duedate RDD, a payout of $50 for the patient and/or condition will berewarded. The refill due date RDD for each patient and theircorresponding condition can be recalculated based on when the patientactually refills the prescription (or even based on an estimated refilldate) taking into account their supply that was filled. In this example,if “Jane Armstrong” in fact refills the prescription on Apr. 24, 2014,then the next refill due date RDD would be approximately 90 days fromApr. 24, 2014 (i.e., Jul. 23, 2014).

The interface shown in FIG. 8 can also show different factors forassessing a patient PID. For example, a user could select a list ofpatient assessment features PAF as to why a particular patient has notbeen consistently adherent to a medication. For example, the patientcould have had difficulty reaching the pharmacy and/or the patient didnot understand the dosing instructions. It should be appreciated thatthe list shown in FIG. 8 is only an example and more/less factors can belisted and selected. Likewise, the interface allows for a user tomanually enter the particular assessment in a text entry box.

FIG. 9 shows an example interface for showing certain key facts KF1, KF2for a particular patient. In the example shown in FIG. 9, some key factsKF1, KF2 for this patient include that the patient over the course ofthe last year has had 5 medications in 5 therapy classes as well as 2prescribers and 2 pharmacies. The key facts section includes a variableset of facts about the patient based on the medical and pharmacy dataaccumulated. The key facts portion has options for “displaying always”and “displaying when relevant” which includes different informationdepending upon the option. This information can be useful in helping todetermine why or why not a patient has been adherent to a particulardrug regimen. It should be appreciated that the key facts can be derivedfrom data contained within a claims data feed obtained to performpredictions and prioritize patients.

FIG. 10 shows an example interface providing a summary of overallpatient adherence as well as the overall bonus payment for a givenpractitioner PRID. The interface can show a practitioner summary PRSduring a given quarter showing, at the least, the type of therapyprovided, a number of patients being treated for the therapy, and anaverage number of untreated days for the type of therapy in the summaryPRS. In the example shown in FIG. 10, the practitioner PRID has 19patients being treated for diabetes where the overall average number ofuntreated days for the collection of patients is 6.47 days. The displaycan also list a total number of therapies as well as the averageuntreated days for all therapies.

In addition to the practitioner summary PRS, the interface can also showa practitioner bonus summary PRBS showing missed and remaining bonuspayment amounts for a given period of time. In the example shown in FIG.10, the practitioner bonus summary extends across a calendar year andshows the missed and remaining bonus payments for a particular quarter(e.g., quarter Q2). Similar to the summary shown in FIG. 6, practitionerPRID has missed $750 in bonus payments where $2,750 in bonus paymentsare remaining. The summary can also “drill down” to show missed andremaining bonus payments for individual conditions/therapies. It shouldbe appreciated that the summary is not limited to only showing displayfor a calendar year and can extend for a period of time that is longeror shorter. Likewise, the time period does not necessarily have to bedivided into quarters and can be shown as a single period of time or inother different time subdivisions (e.g., monthly). Furthermore, thedisplay could also show the bonus payment amount achieved for a givenpractitioner during a particular time period.

FIG. 11 shows an example interface providing a practitioner summarylevel view showing one or more non-participating practices. It should beappreciated that the display could instead show one or moreparticipating practices.

FIG. 11 illustrates a tool for allowing executive users and liaisons ofcustomers to manage and track the progress of practices in the system.Through the tool, they can view the practices onboarding, engagement,and outcome status, as well as key operational information such as thenumber of users in the practice, their contact information, the numberof prescribers in the practice, their contact information, the paymentaddress of the practice, etc.

In the example shown in FIG. 11, each practitioner PRID is listed andgiven an engagement score ES as to how engaged the practitioner PRID isin helping the patients to become adherent to their respective drugregimens. The summary can also list the specific practitioner namePRNAME, the practitioner region PRREGION, and/or the bonus eligiblepatients PRBEP for each practitioner. The display can also include otherdata (e.g., time periods of participation, liaisons) and is not limitedto the information described above.

In the example shown in FIG. 11, each practitioner is ranked based ontheir engagement score ES which is shown, for example, based on a “star”ranking system. For example, a highly engaged practitioner will havefour stars, a regularly engaged practitioner will have three stars, asomewhat engaged practitioner will have two stars, and a non-engagedpractitioner will have only one star. Practitioners that are notapplicable for engagement can be listed with designation “N/A.”

The engagement score ES is based on a composite score that may, forexample, include, but is not limited to, the following example practiceactivity metrics: (1) logging into the system on a regular basis (logintotal adjusted for number of users); (2) user has logged in during thepast two weeks; (3) users are accessing their patient list; and/or (4)one or more engaged users (e.g., engaged users login to the system on aregular basis, have on average longer visits and use the system in moredetail than other users). The practice can receive “points” for thevarious activity metrics noted above, which ultimately determines theiroverall engagement category (e.g., “star” ranking).

Practices are considered highly engaged if they are logging into thesystem on a regular basis, have logged in during the past two weeks, areaccessing their patient list, and have at least one engaged user. Theseare stand-out practices that are excelling at all of the ways the systemmeasures activity above and beyond how the average ‘engaged’ practicesare performing.

Practices are considered engaged if users login to the system on aregular basis, have on average longer visits and use the system in moredetail than other users. Practices are considered somewhat engaged ifthey are logging into the system and accessing their patient list, buton an irregular basis. Finally, not engaged practices are not logginginto the system, have not logged in during the past two weeks, are notaccessing their patient list, and do not have any engaged users. Theinterface in FIG. 11 is useful in that it can show a user how gooddifferent practitioners are at making their patients adherent to one ormore drug regimens without having to individually examine eachpractitioner.

FIG. 12 shows another example interface summarizing practice informationfor a given practitioner PRID. The display can show the engagement scoreES for the practitioner PRID as well as the number of bonus eligiblepatients PRBEP for a given practitioner PRID. Among other pieces ofinformation, the interface can also show the list of providers PRL foreach practitioner PRID where contact information is provided for therespective provider PRL as well as the number of members attended to bythe provider PRL. The interface can also be configured to provide anengagement score for each individual provider PRL to determine whichspecific providers are more or less engaged than others.

It should be appreciated that the above-describe techniques are notlimited to applying the incentive to each patient and could be appliedto, for example, a group of patients. As an example, a scenario couldexist where a practice has 10 patients to manage and the system does notapply a per-patient bonus. Instead, the system could provide an overallperformance bonus. For example, if at an end of a quarter all 10patients are adherent, the practice could receive a total of $1,000; if9 are adherent, the practice receives $900; if 8 are adherent, thepractice receives $800; and if 7 are adherent, the practice receives$700. If the practice has less than 7 that are adherent, the system maynot award the practice any bonus (i.e., $0). Likewise, the practicecould receive the entire bonus amount of a minimum threshold in the poolof patients is satisfied (e.g., if the practice has at least 7 patientsbeing adherent they receive the full $1,000).

Such a configuration is advantageous in situations where the expectednumber of patients that are adherent without intervention is at aminimum amount. Using the example above, this configuration would bebeneficial in situations where 6 of the 10 patients at the practicewould be adherent without any intervention. Thus, under thisconfiguration the practice will receive no credit unless they surpasstheir expected adherence rate. Such a configuration could eliminate“dead-weight” losses of the program and actually allow larger bonusesfor those remaining (e.g., if the system channels all the bonus paid tothe patient who would already have been adherent to the bonus amount iftheir panel rate of adherence exceeds an expected value). Thus, such aconfiguration advantageously prevents practices from participating inthe program without actually performing no intervention (and stillgetting paid the bonus amount).

As discussed above, the technology described in this application isdirected to an iterative data process comprising a) prediction, b)patient selection/prioritization, c) and evaluation analytics tocontinuously optimize performance on medication quality indicators at apopulation level, including medication adherence. This technology usespatients' predicted future medication outcomes, other patientcharacteristics derived from patient data, and intervention capacityattributes to compute the most cost-effective interventionrecommendation for each individual patient on a recurring basis based onaccumulating data.

The technology aims to achieve increased patient adherence to selectedmedication regimens and improvements in other quality and efficiencymeasures in defined patient populations. For example, this technology isequally effective in other medical services (which could includeservices related to both adherence as well as non-adherence), including,but not limited to, mammogram services, prostate exams, services forpatients at risk of opioid (e.g., pain medication) overutilization,services for patients taking two more drugs that conflict with eachother and should not be taken together, services for patients onexcessive amounts of “high risk medications,” services for patients atrisk of not taking certain medications as directed (e.g., patients withbehavioral health diagnoses who are at risk of not taking prescribedmedications), and/or other behavioral health issues includingdepression, attention deficit disorder (ADHD), and/or bipolar disorder.The system is also effective for specialty medications that payers couldbe concerned about from a cost and/or utilization perspective including,but not limited to, drugs to treat Hepatitis C, MS, Crohn's, and others.

The system described herein is also effective for other various medicalservices including services related to reducing hospital readmissions.For example, the system could target incentives on behalf of members whoare predicated to have a strong likelihood of being readmitted.Likewise, this system could also be employed in apharmacy/pharmaceutical setting. In such a scenario, the incentive planmay not necessarily apply (e.g., the plan may not be responsible forproviding incentives) and/or the user interface could be configured sothat it is made available to pharmacy staff (e.g., instead of staff fora health care provider).

The technical field is health care and health care data analytics. Thetechnology helps facilitate efficient population-level improvements inmedication adherence and other health care outcomes. The technologyleverages the use of patient-level predictions about future health careoutcomes to inform the design and delivery of patient engagementactivities.

The technology establishes the basis for innovative, prediction-drivenperformance improvement programs in health care that have the potentialto improve the prevalence and performance of health care financial bonusprograms for health care professionals designed to achieve targetedimprovements in health care outcomes, including medication adherencecommensurate with the value that those very targeted improvements createto sponsors of such programs. The technology constitutes the coreenabler of new products that support health care improvement programsusing financial incentives to encourage new and/or better informedactions by health care professionals to engage patients in new ways,including taking their medications consistently as prescribed.

In the above-description, for purposes of explanation andnon-limitation, specific details are set forth, such as particularnodes, functional entities, techniques, protocols, standards, etc. inorder to provide an understanding of the described technology. It willbe apparent to one skilled in the art that other embodiments may bepracticed apart from the specific details described below. In otherinstances, detailed descriptions of well-known methods, devices,techniques, etc. are omitted so as not to obscure the description withunnecessary detail. Individual function blocks are shown in the figures.Those skilled in the art will appreciate that the functions of thoseblocks may be implemented using individual hardware circuits, usingsoftware programs and data in conjunction with a suitably programmedmicroprocessor or general purpose computer, using applications specificintegrated circuitry (ASIC), and/or using one or more digital signalprocessors (DSPs). The software program instructions and data may bestored on computer-readable storage medium and when the instructions areexecuted by a computer or other suitable processor control, the computeror processor performs the functions. Although databases may be depictedas tables below, other formats (including relational databases,object-based models, and/or distributed databases) may be used to storeand manipulate data.

Although process steps, algorithms or the like may be described orclaimed in a particular sequential order, such processes may beconfigured to work in different orders. In other words, any sequence ororder of steps that may be explicitly described or claimed does notnecessarily indicate a requirement that the steps be performed in thatorder. The steps of processes described herein may be performed in anyorder possible. Further, some steps may be performed simultaneouslydespite being described or implied as occurring non-simultaneously(e.g., because one step is described after the other step). Moreover,the illustration of a process by its depiction in a drawing does notimply that the illustrated process is exclusive of other variations andmodifications thereto, does not imply that the illustrated process orany of its steps are necessary to the technology, and does not implythat the illustrated process is preferred.

Various forms of computer readable media/transmissions may be involvedin carrying data (e.g., sequences of instructions) to a processor. Forexample, data may be (i) delivered from RAM to a processor; (ii) carriedover any type of transmission medium (e.g., wire, wireless, optical,etc.); (iii) formatted and/or transmitted according to numerous formats,standards or protocols, such as Ethernet (or IEEE 802.3), SAP, ATP,Bluetooth, and TCP/IP, TDMA, CDMA, 3G, etc.; and/or (iv) encrypted toensure privacy or prevent fraud in any of a variety of ways well knownin the art.

While the technology has been described in connection with what ispresently considered to be an illustrative practical and preferredembodiment, it is to be understood that the technology is not to belimited to the disclosed embodiment, but on the contrary, is intended tocover various modifications and equivalent arrangements.

The invention claimed is:
 1. A system configured to identify a patientpopulation in a healthcare program based on, in part, a predictivefunction applied to characteristics of a patient, the system comprising:a processing system having at least: a processor, a communicationsinterface, and a memory storing computer readable instructions that,when executed by the processor, cause or enable the system to: establishboundaries for an incentive program based on, at least, rules associatedwith the incentive program; determine a list of health care providersand patients eligible for participation in the incentive program; obtainone or more data files including historical data for each patient; applythe historical data to one or more variables and generate a resultantdata file; apply the resultant data file to a model setup procedure togenerate a prediction function; generate an adherence risk score foreach patient using the prediction function and apply the score to eachpatient in the list of patients; identify a subset of patients from theone or more patients based on, at least, factors related to a likelihoodof adherence after a given intervention is carried out; and generate auser interface for display providing, at least, a prioritized listing ofone or more patients from the subset of patients, one or more medicalconditions associated with each of the one or more patients, and a bonusopportunity associated with each of the one or more patients, wherein atleast one practitioner is identified for each patient and the respectivepatient is assigned to the practitioner in order to help the patientachieve medication adherence, and the system is configured to excludepatients that are highly likely to achieve a desired health outcomewithout intervention and exclude patients that are highly unlikely toachieve a desired health outcome with intervention.
 2. The system ofclaim 1, wherein the processing system is further configured to generatea custom incentive associated with each patient in the subset ofpatients.
 3. The system of claim 2, wherein the processing system isfurther configured to: generate the bonus opportunity associated witheach patient in the subset of patients based on at least the adherencerisk score for each patient relative to one or more conditions relatingto the health of the patient.
 4. The system of claim 1, wherein the userinterface is further configured to display at least a list of conditionsrelated to the health of each patient in the subset and/or a number ofuntreated days for each patient for each eligible condition.
 5. Thesystem of claim 1, wherein the processing system is further configuredto filter the subset of candidates to candidates that are unlikely toachieve a desired health outcome without intervention and candidateswho, if achieve the desired health outcome, would aid in maximizing abenefit of the incentive program.
 6. The system of claim 1, wherein theone or more variables are defined based on time periods of interest,dependent variables associated with a proportion of days covered,medication possession ratio, or discontinuation, and independentvariables associated with attributes of a patient, attributes of atarget drug regimen, and attributes of a health care system.
 7. Thesystem of claim 1, wherein the model setup procedure includes dividingthe resultant data file into a training data file and a validation datafile.
 8. The system of claim 7, wherein the prediction function isgenerated by using the training data file over multiple statisticalmethods and then testing the prediction function against the validationdata file.
 9. A method implemented using at least an informationprocessing apparatus having at least one processor, a memory, and acommunications interface, the method comprising: determining a list ofcandidates having one or more medical therapies associated with eachcandidate; obtaining one or more data files including historical datafor each patient; applying the historical data to one or more variablesand generate a resultant data file; applying the resultant data file toa model setup procedure to generate a prediction function; generating anadherence risk score for each candidate using the prediction functionand applying the adherence risk score to each candidate in the list ofcandidates; identifying a subset of candidates from the one or morecandidates based on, at least, factors related to a likelihood ofadherence after a given intervention is carried out; prioritizing thesubset of candidates based on, at least, the adherence risk scoreapplied to each candidate in the subset of candidates; and generating auser interface for display providing the prioritized listing of thesubset of candidates and at least one medical therapy associated witheach candidate in the subset of candidates.
 10. The method of claim 9,further comprising generating a custom incentive associated with eachcandidate in the subset of candidates.
 11. The method of claim 10,further comprising: generating a bonus opportunity associated with eachcandidate in the subset of candidates based on at least the adherencerisk score for each candidate relative to the one or more medicaltherapies associated with each candidate; and generating for display atleast the bonus opportunity for each candidate in the subset ofcandidates.
 12. The method of claim 10, wherein the user interface isfurther configured to display the subset of candidates and at least onevariable related to the custom incentive for each candidate in thesubset of candidates.
 13. The method of claim 12, wherein the userinterface is further configured to display at least a number ofuntreated days for each candidate for each eligible condition.
 14. Themethod of claim 9, wherein generating the adherence risk score for eachcandidate comprises: defining relevant time periods for analyzing saidhistorical data; defining a dependent variable indicative of a measureto be predicted; determining at least one independent predictorvariable; creating a resultant data file including said dependent andindependent variables; and deriving the adherence risk score based onsaid resultant data.
 15. A non-transitory computer-readable storagemedium storing a program executable by a computer having at least oneprocessor, the program when executed causing the computer to provideexecution comprising: determining a list of candidates having one ormore medical therapies associated with each candidate; generating aprediction function by applying a resultant data file to a model setupprocedure, the resultant data file generated by applying historical datato one or more variables; generating an adherence risk score for eachcandidate using the prediction function and applying the adherence riskscore to each candidate in the list of candidates; identifying a subsetof candidates from the one or more candidates based on, at least,factors related to a likelihood of adherence after a given interventionis carried out; prioritizing the subset of candidates based on, atleast, the adherence risk score applied to each candidate in the subsetof candidates; and generating a user interface for display providing theprioritized listing of the subset of candidates and at least one medicaltherapy associated with each candidate in the subset of candidates. 16.The non-transitory computer-readable storage medium of claim 15, whereinthe program further causing the computer to provide execution comprisinggenerating a custom incentive associated with each candidate in thesubset of candidates.
 17. The non-transitory computer-readable storagemedium of claim 16, wherein the program further causing the computer toprovide execution comprising: generating a bonus opportunity associatedwith each candidate in the subset of candidates based on at least theadherence risk score for each candidate relative to the one or moremedical therapies associated with each candidate; and generating fordisplay at least the bonus opportunity for each candidate in the subsetof candidates.
 18. The non-transitory computer-readable storage mediumof claim 16, wherein the user interface is further configured to displayat least one variable related to the custom incentive for each candidatein the subset of candidates.
 19. The non-transitory computer-readablestorage medium of claim 18, wherein the user interface is furtherconfigured to display a number of untreated days for each candidate foreach eligible condition.
 20. The non-transitory computer-readablestorage medium of claim 15, wherein generating the adherence risk scorefor each candidate comprises: defining relevant time periods foranalyzing said historical data; defining a dependent variable indicativeof a measure to be predicted; determining at least one independentpredictor variable; creating a resultant data file including saiddependent and independent variables; and deriving the adherence riskscore based on said resultant data.